Impact of glandular hair exudates on infection of chickpea by <i>Ascochyta rabiei</i>
Bibliographic record
Abstract
Ascochyta blight, a fungal disease caused by Ascochyta rabiei (Pass.) Labr. (teleomorph Didymella rabiei (Kovačevski) v. Arx), is a major constraint to chickpea (Cicer arietinum L.) production. An unusual feature of chickpea plants is the secretion of a highly acidic fluid from glandular trichomes. This exudate was shown to promote germination of conidia of A. rabiei at low concentrations (0.012 and 0.06 mg·mL1) but to inhibit germination at higher concentrations (0.3 and 1.5 mg·mL1). The removal of exudates from plants prior to inoculation with A. rabiei increased infection and, subsequently, disease development in susceptible cultivars. On partially resistant cultivars, however, the effect of removing exudates on infection was not apparent until early podding, when genetic resistance declines. Simulated rain applied prior to inoculation increased disease severity as the volume of simulated rain increased from 1 to 4 mm. Since rainfall is required for dispersal of conidia, host exudates are likely to have little impact on secondary disease spread under field conditions. Exudates may, however, contribute to variability in disease studies conducted on older chickpea plants, especially in controlled environments.Key words: ascochyta blight, epidemiology, screening, Didymella rabiei, Cicer arietinum.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".